code: def test_create_promptlab_run(store):
      exp_id = store.create_experiment("test_create_promptlab_run")
      run = _create_promptlab_run_impl(
          store,
          experiment_id=exp_id,
          run_name="my_promptlab_run",
          tags=[],
          prompt_template="my_prompt_template",
          prompt_parameters=[
              Param("prompt_param_key", "prompt_param_value")
          ],
          model_route="my_route",
          model_parameters=[Param("temperature", "0.1")],
          model_input="",
          model_output_parameters=[
              Param("output_param_key", "output_param_value")
          ],
          model_output="my_output",
          mlflow_version="1.0.0",
          user_id="user",
          start_time=1,
      )
      assert run.info.run_id is not None
      assert run.info.status == RunStatus.to_string(RunStatus.FINISHED)
  
      assert run.data.params["prompt_template"] == "my_prompt_template"
      assert run.data.params["model_route"] == "my_route"
      assert run.data.params["temperature"] == "0.1"
  
      assert run.data.tags["mlflow.runName"] == "my_promptlab_run"
      assert (
          run.data.tags["mlflow.loggedArtifacts"]
          == '[{"path": "eval_results_table.json", "type": "table"}]'
      )
      assert (
          run.data.tags["mlflow.runSourceType"] == "PROMPT_ENGINEERING"
      )
      assert run.data.tags["mlflow.log-model.history"] is not None
  
      # list the files in the model folder
      artifact_location = run.info.artifact_uri
      artifact_repo = get_artifact_repository(artifact_location)
  
      artifact_files = [f.path for f in artifact_repo.list_artifacts()]
      assert "eval_results_table.json" in artifact_files
      assert "model" in artifact_files
  
      model_files = [
          f.path for f in artifact_repo.list_artifacts("model")
      ]
      assert "model/MLmodel" in model_files
      assert "model/python_env.yaml" in model_files
      assert "model/conda.yaml" in model_files
      assert "model/requirements.txt" in model_files
      assert "model/input_example.json" in model_files
  
      # try to load the model
      import mlflow.pyfunc
  
      mlflow.pyfunc.load_model(os.path.join(artifact_location, "model"))
  
file_path: mlflow/tests/utils/test_promptlab_utils.py
imports: import json
  import os
  import pytest
  from mlflow.entities.param import Param
  from mlflow.entities.run_status import RunStatus
  from mlflow.store.artifact.artifact_repository_registry import get_artifact_repository
  from mlflow.store.tracking.file_store import FileStore
  from mlflow.utils.promptlab_utils import _create_promptlab_run_impl, create_eval_results_json
label_desc: Use image node_id 3 for calling a global function with example usage: test_create_promptlab_run(store) without return types